Towards Smart Homes Using Low Level Sensory Data

Khattak, Asad Masood; Truc, Phan Tran Ho; Hưng, Lê Xuân; Dang, Viet-hung; Guan, Donghai; Pervez, Zeeshan; Han, Myung-Mook; Lee, Sungyoung Lee.; Lee, Young-Koo

doi:10.3390/s111211581

Cited by 26 publications

(39 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The currently most used paradigm is to measure raw data from the sensor and send the data to the cloud, or a computer, in order to be processed by complex algorithms [3,4]. Another school behind those devices is to process sensor data within the sensor and provide the user with a readily usable, filtered and normalized measurement.…”

Section: Introductionmentioning

confidence: 99%

A Study about Kalman Filters Applied to Embedded Sensors

Valade

Acco

Grabolosa³

et al. 2017

Sensors

View full text Add to dashboard Cite

Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate.

show abstract

Section: Introductionmentioning

confidence: 99%

A Study about Kalman Filters Applied to Embedded Sensors

Valade

Acco

Grabolosa³

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…It has the capability to capture context information and derive high-level activities based on low-level sensory data. 13 The system has been evaluated for monitoring Alzheimer's disease patients and provided with basic non-medication recommendations. It was lacking interfaces for medication intervention, and the physician role was ignored for managing patient activities.…”

Section: Introductionmentioning

confidence: 99%

Recommendations Service for Chronic Disease Patient in Multimodel Sensors Home Environment

Hussain

Ali²,

Khan³

et al. 2015

Telemedicine and e-Health

Self Cite

View full text Add to dashboard Cite

With advanced technologies in hand, there exist potential applications and services built around monitoring activities of daily living (ADL) of elderly people at nursing homes. Most of the elderly people in these facilities are suffering from different chronic diseases such as dementia. Existing technologies are mainly focusing on non-medication interventions and monitoring of ADL for addressing loss of autonomy or well-being. Monitoring and managing ADL related to cognitive behaviors for non-medication intervention are very effective in improving dementia patients' conditions. However, cognitive functions of patients can be improved if appropriate recommendations of medications are delivered at a particular time. Previously we developed the Secured Wireless Sensor Network Integrated Cloud Computing for Ubiquitous-Life Care (SC 3

show abstract

“…Context-aware systems adapt their operations and services to the user’s context without explicit intervention from the user. This extra feature of making use of the user’s environment/context for the purpose of computation has increased the usability and effectiveness of such systems and has encouraged many researchers to contribute in this research domain [2,6,7]. …”

Section: Introductionmentioning

confidence: 99%

Context Representation and Fusion: Advancements and Opportunities

Khattak

Akbar

Aazam

et al. 2014

Sensors

Self Cite

View full text Add to dashboard Cite

The acceptance and usability of context-aware systems have given them the edge of wide use in various domains and has also attracted the attention of researchers in the area of context-aware computing. Making user context information available to such systems is the center of attention. However, there is very little emphasis given to the process of context representation and context fusion which are integral parts of context-aware systems. Context representation and fusion facilitate in recognizing the dependency/relationship of one data source on another to extract a better understanding of user context. The problem is more critical when data is emerging from heterogeneous sources of diverse nature like sensors, user profiles, and social interactions and also at different timestamps. Both the processes of context representation and fusion are followed in one way or another; however, they are not discussed explicitly for the realization of context-aware systems. In other words most of the context-aware systems underestimate the importance context representation and fusion. This research has explicitly focused on the importance of both the processes of context representation and fusion and has streamlined their existence in the overall architecture of context-aware systems’ design and development. Various applications of context representation and fusion in context-aware systems are also highlighted in this research. A detailed review on both the processes is provided in this research with their applications. Future research directions (challenges) are also highlighted which needs proper attention for the purpose of achieving the goal of realizing context-aware systems.

show abstract

Towards Smart Homes Using Low Level Sensory Data

Cited by 26 publications

References 23 publications

A Study about Kalman Filters Applied to Embedded Sensors

A Study about Kalman Filters Applied to Embedded Sensors

Recommendations Service for Chronic Disease Patient in Multimodel Sensors Home Environment

Context Representation and Fusion: Advancements and Opportunities

Contact Info

Product

Resources

About